Read Memories

Use this to recover your own context from past sessions. Do NOT narrate the process to the user -- silently run the query, absorb the results, and continue with enriched context.

$0 is the keyword to search for. Pass --here as an argument to scope the search to the current project only. Pass --geo as an argument to additionally extract geospatial-specific context (EPSG codes, bounding boxes, CRS info, spatial file paths, model names).

Step 1 -- Set the search path

ALL_PROJECTS="$HOME/.claude/projects/*/*.jsonl"
CURRENT_PROJECT="$HOME/.claude/projects/$(echo "$PWD" | sed 's|[/_]|-|g')/*.jsonl"

Use $CURRENT_PROJECT if any argument is --here, otherwise use $ALL_PROJECTS. Store the chosen glob in SEARCH_PATH.

Check whether the --geo flag is present.

Step 2 -- Query with Python

Run the following Python script via python3 -c "...", substituting ` and with the resolved values. Escape any single quotes in ` before embedding it.

python3 -c "
import json, glob, os

SEARCH_PATH = ''
KEYWORD = ''.lower()
LIMIT = 40

files = sorted(glob.glob(os.path.expanduser(SEARCH_PATH)))
results = []

for fpath in files:
    parts = fpath.split('/')
    try:
        proj_idx = parts.index('projects') + 1
        project = parts[proj_idx] if proj_idx = LIMIT:
                break
    if len(results) >= LIMIT:
        break

print(f'Found {len(results)} results (limit {LIMIT})')
print('---')
for i, r in enumerate(results):
    print(f'[{i+1}] project={r[\"project\"]} ts={r[\"ts\"]} role={r[\"role\"]}')
    print(r['content'][:800])
    print('---')
"

Step 3 -- Handle large result sets

If Step 2 reports exactly 40 results (limit hit), the keyword is common. Run a counting pass to understand the scope:

python3 -c "
import json, glob, os

SEARCH_PATH = ''
KEYWORD = ''.lower()

files = sorted(glob.glob(os.path.expanduser(SEARCH_PATH)))
total = 0
by_project = {}

for fpath in files:
    parts = fpath.split('/')
    try:
        proj_idx = parts.index('projects') + 1
        project = parts[proj_idx] if proj_idx '
KEYWORD = ''.lower()

patterns = {
    'epsg_codes': re.compile(r'EPSG[:\s]*(\d{4,5})', re.IGNORECASE),
    'bbox': re.compile(r'(?:bbox|bounding.?box|bounds)\s*[=:]\s*\[([^\]]+)\]', re.IGNORECASE),
    'crs': re.compile(r'(?:CRS|SRS|projection)\s*[=:]\s*[\"\\']?([^\"\\'\\n,;]{3,60})', re.IGNORECASE),
    'spatial_files': re.compile(r'[\w/.-]+\.(?:shp|gpkg|geojson|tiff?|nc|hdf[45]?|gdb|fgb|kml|las|laz|parquet)', re.IGNORECASE),
    'coords': re.compile(r'(?:lat(?:itude)?|lon(?:gitude)?|lng)\s*[=:]\s*(-?\d+\.?\d*)', re.IGNORECASE),
    'models': re.compile(r'(?:sam2?|segment.?anything|yolo\w*|resnet\w*|u-?net|deeplabv3|mask.?rcnn|faster.?rcnn|swin|vit|dinov?\d?|geoclip|satlas|clay|prithvi)', re.IGNORECASE),
    'resolutions': re.compile(r'(\d+(?:\.\d+)?)\s*(?:m|meter|cm|km)\s*(?:resolution|pixel|spacing)', re.IGNORECASE),
}

files = sorted(glob.glob(os.path.expanduser(SEARCH_PATH)))
findings = {k: set() for k in patterns}

for fpath in files:
    with open(fpath, 'r', errors='replace') as f:
        for line in f:
            try:
                obj = json.loads(line)
            except (json.JSONDecodeError, ValueError):
                continue
            msg = obj.get('message')
            if not isinstance(msg, dict):
                continue
            role = msg.get('role')
            if role not in ('user', 'assistant'):
                continue
            content = msg.get('content', '')
            if isinstance(content, list):
                text = ' '.join(
                    c.get('text', '')
                    for c in content
                    if isinstance(c, dict) and 'text' in c
                )
            elif isinstance(content, str):
                text = content
            else:
                continue
            if KEYWORD not in text.lower():
                continue

            for name, pat in patterns.items():
                for m in pat.finditer(text):
                    findings[name].add(m.group(0).strip())

print('=== Geospatial Context ===')
for name, vals in findings.items():
    if vals:
        print(f'{name}:')
        for v in sorted(vals)[:20]:
            print(f'  - {v}')
"

Step 5 -- Internalize

From the results, extract:

• Decisions made and their rationale
• Patterns and conventions established (coordinate systems, data formats, naming)
• Data file paths and datasets previously used
• CRS/EPSG codes that were chosen and why
• Bounding boxes or areas of interest
• Model configurations (architecture, hyperparameters, checkpoints)
• Unresolved items or open TODOs
• Any corrections the user made to your prior behavior

Use this to inform your current response. Do not repeat back the raw logs to the user.

Notes

• No external dependencies: This skill uses only Python standard library

modules (json, glob, os, re). No pip install is needed.

• Privacy: All data stays local. Nothing is sent over the network.
• Content types: The search covers both user messages and assistant

responses. It skips system messages, tooluse blocks, and toolresult blocks (only the text type within content arrays is extracted).

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: opengeos
• Source: opengeos/geoai-skills
• License: MIT

Install and usage instructions live in the source repository linked above.